Q-learning [1]} is a popular learning algorithm that can be applied to most sequential tasks to learn the state-action value. It is an off-policy learning algorithm, as the policy being learnt is not executed during training, instead a behaviour policy is used which is exploratory in nature. But the target action is the one predicted by the policy using the following update rule iteratively at each time step \(t\) :
\(Q(s,a) \leftarrow Q(s,a) + \alpha (r+\gamma Q(s^{\prime },a^{\prime }) - Q(s,a))\) 
